Merge branch 'master' into delete_ones

extra/gemm/amd_uop_matmul.py

@@ -1,10 +1,9 @@
 from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, graph_rewrite, AxisType
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, graph_rewrite
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from tinygrad.dtype import AddrSpace
 from tinygrad.schedule.kernelize import merge_views
-from tinygrad.helpers import getenv, colored
-from tinygrad.shape.shapetracker import ShapeTracker
+from tinygrad.helpers import getenv
 
 N = 4096
 run_count = 5
@@ -21,16 +20,13 @@ def hl_spec_kernel3():
   nbIterWaveN = 2
 
   # define buffers
-  # TODO: remove these views once the defines have a shape
-  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0).view(ShapeTracker.from_shape((N,N)))
-  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1).view(ShapeTracker.from_shape((N,N))).permute((1,0))
-  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2).view(ShapeTracker.from_shape((N,N)))
-  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM, AddrSpace.LOCAL), arg=0).view(ShapeTracker.from_shape((BK*BM,)))
-  Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1).view(ShapeTracker.from_shape((BK*BN,)))
-  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((nbIterWaveM * TM,)))
-  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1).view(ShapeTracker.from_shape((nbIterWaveN * TN,)))
-  acc = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM * nbIterWaveN * TN, AddrSpace.REG), arg=2) \
-    .view(ShapeTracker.from_shape((nbIterWaveM * TM * nbIterWaveN * TN,)))
+  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0)
+  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1)
+  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2)
+  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM, AddrSpace.LOCAL), arg=0)
+  Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1)
+  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0)
+  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1)
 
   # shape buffers. TODO: permutes
   full_shape = (N//BM, nbIterWaveM, BM//(nbIterWaveM * TM), TM, N//BN, nbIterWaveN, BN//(nbIterWaveN * TN), TN, N//BK, BK)
@@ -42,24 +38,8 @@ def hl_spec_kernel3():
   A_col = A_col.reshape((1, nbIterWaveM, 1, TM, 1, 1, 1, 1, 1, 1)).expand(full_shape)
   B_row = B_row.reshape((1, 1, 1, 1, 1, nbIterWaveN, 1, TN, 1, 1)).expand(full_shape)
 
-  acc = acc.reshape((1, nbIterWaveM, 1, TM, 1, nbIterWaveN, 1, TN, 1, 1)).expand(full_shape[:-2]+(1,1))
-
-  #out = a.load() * b.load()
-  #out = As.load(As.store(a.load())) * Bs.load(Bs.store(b.load()))
-  out = A_col.load(A_col.store(As.load(As.store(a.load())))) * B_row.load(B_row.store(Bs.load(Bs.store(b.load()))))
-
-  #out = out.r(Ops.ADD, (8, 9))
-  out = UOp(Ops.REDUCE_INTO, out.dtype, (acc, out), Ops.ADD)
-
-  axis_types = (
-    AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
-    AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
-    AxisType.REDUCE, AxisType.UNROLL)
-
-  from tinygrad.opt.kernel import axis_colors
-  shape = '_'.join([colored(str(s), axis_colors[at]) for s,at in zip(full_shape, axis_types)])
-  print(shape)
-  sink = c.store(out).sink(arg=KernelInfo(name="tg_"+shape, axis_types=axis_types))
+  out = (A_col.store(As.store(a.load()).load()).load() * B_row.store(Bs.store(b.load()).load()).load()).r(Ops.ADD, (8, 9))
+  sink = c.store(out).sink(arg=KernelInfo(name="tinygemm"))
   sink = graph_rewrite(sink, merge_views)
   return sink
 
@@ -152,8 +132,8 @@ def hand_spec_kernel3():
 
   # do the GEMM math
   iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 8)
-  yt = UOp.range(dtypes.int, TM, 9)
-  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 10)
+  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 9)
+  yt = UOp.range(dtypes.int, TM, 10)
   xt = UOp.range(dtypes.int, TN, 11)
   x = iterWaveN * TN + xt
   y = iterWaveM * TM + yt
@@ -163,8 +143,8 @@ def hand_spec_kernel3():
 
   # store c_regs into c
   iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 12)
-  yt = UOp.range(dtypes.int, TM, 13)
-  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 14)
+  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 13)
+  yt = UOp.range(dtypes.int, TM, 14)
   xt = UOp.range(dtypes.int, TN, 15)
   xOut = blockIdx_x * BN + waveIdx * WN + iterWaveN * SUBWN + TN * idxInWave
   yOut = blockIdx_y * BM + waveIdy * WM + iterWaveM * SUBWM + TM * idyInWave
@@ -178,7 +158,6 @@ if __name__ == "__main__":
   hprg = hl_spec_kernel3() if getenv("HL") else hand_spec_kernel3()
   prg = get_program(hprg, Device.default.renderer)
   print(prg.src)
-  exit(0)
   hrunner = CompiledRunner(prg)
 
   a = Tensor.randn(N, N).realize()

test/test_define_reg.py

@@ -1,42 +0,0 @@
-import unittest
-from tinygrad import dtypes, Device, Tensor, Context
-from tinygrad.dtype import AddrSpace
-from tinygrad.helpers import getenv
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.engine.realize import get_program, ExecItem, CompiledRunner
-
-class TestDefineReg(unittest.TestCase):
-  def test_simple(self, at=AxisType.UPCAST):
-    N = 16
-    bout = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0).view(ShapeTracker.from_shape((N,N)))
-    a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1).view(ShapeTracker.from_shape((N,N)))
-    a_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(N, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((N,N), (0,1)))
-
-    out = a_col.load(a_col.store(a.load()))
-    sink = bout.store(out).sink(arg=KernelInfo(name="regcopy", axis_types=(AxisType.LOOP, at)))
-    prg = get_program(sink, Device.default.renderer)
-
-    with Context(DEBUG=0):
-      a = Tensor.randn(N, N).realize()
-      b = Tensor.empty(N, N).realize()
-    hrunner = CompiledRunner(prg)
-    ExecItem(hrunner, [b.uop.buffer, a.uop.buffer]).run(wait=True)
-    with Context(DEBUG=0):
-      self.assertEqual((b-a).mean().item(), 0.0)
-
-  @unittest.skipIf(getenv("PTX"), "ptx needs regs to be unrolled")
-  def test_simple_loop(self): self.test_simple(AxisType.LOOP)
-
-  def test_reduce_into(self):
-    N = 16
-    b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N*N), arg=0).view(ShapeTracker.from_shape((N,N,1)))
-    a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N*N), arg=1).view(ShapeTracker.from_shape((N,N,N)))
-    a_reg = UOp(Ops.DEFINE_REG, dtypes.float.ptr(N, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((N,N,1), (0,1,0)))
-    ret = UOp(Ops.REDUCE_INTO, dtypes.float, src=(a_reg, a.load()), arg=Ops.ADD)
-    sink = b.store(ret).sink(arg=KernelInfo(name="regcopy"))
-    prg = get_program(sink, Device.default.renderer)
-    print(prg.src)
-
-if __name__ == '__main__':
-  unittest.main()

test/test_linearizer.py

@@ -291,7 +291,7 @@ class TestLinearizer(unittest.TestCase):
     realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
     program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
 
-    stores = [u for u in program.uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
+    stores = [u for u in program.uops if u.op is Ops.STORE and u.dtype.addrspace != AddrSpace.REG]
 
     # the first store is to lds and can be upcasted
     assert stores[0].src[1].dtype == dtypes.float.vec(4)
@@ -633,7 +633,6 @@ class TestLinearizer(unittest.TestCase):
     helper(Tensor.arange(255), max_ops=2)
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_grouped_store_phis(self):
     """
     float4 acc0 = float4(0.0,0.0,0.0,0.0);
@@ -649,7 +648,7 @@ class TestLinearizer(unittest.TestCase):
     k = helper_linearizer_opt(out)[-1]
     uops = get_program(k.get_optimized_ast(), k.opts).uops
     # check that the float4 cast collapses
-    store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
+    store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.dtype.addrspace != AddrSpace.REG]
     for val in store_vals:
       assert val.dtype == dtypes.float.vec(4) # and val.op is not Ops.VECTORIZE
 
@@ -700,13 +699,12 @@ class TestLinearizer(unittest.TestCase):
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_grouped_store_local_only(self):
     x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
     r = (x@y).relu()
     k = helper_linearizer_opt(r)[-1]
     uops = get_program(k.get_optimized_ast(), k.opts).uops
-    stores = [u for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
+    stores = [u for u in uops if u.op is Ops.STORE and u.dtype.addrspace != AddrSpace.REG]
 
     # the float4 value stores directly in lds and we skip upcast
     self.assertEqual(stores[0].src[1].dtype, dtypes.float.vec(4))

test/test_multitensor.py

@@ -1126,7 +1126,6 @@ class TestMultiRamUsage(unittest.TestCase):
     # NOTE: the first one on the DEFAULT device should be freed
     self.assertUsed(self.N*self.N*4*2)
 
-  @unittest.skip("flaky")
   def test_zeros_shard(self, devices=(d1, d2)):
     _ = Tensor.zeros(self.N, self.N).contiguous().shard(devices, axis=0).realize()
     self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage

tinygrad/codegen/devectorizer.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass
 from tinygrad.dtype import dtypes, ImageDType, PtrDType, DType, AddrSpace
 from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, graph_rewrite, GroupOp, identity_element
 from tinygrad.uop.symbolic import split_uop, uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
-from tinygrad.helpers import getenv, flatten, AMX, prod, partition
+from tinygrad.helpers import getenv, flatten, AMX, prod, partition, all_same
 from tinygrad.renderer import Renderer
 
 # ***** image load valid simplification *****
@@ -111,7 +111,11 @@ def cat_after_store(cat:UOp, data:UOp, sto:UOp):
   for s in cat.src:
     ret.append(s.store(data.gep(tuple(range(offset, offset+s.dtype.count))), *sto.src[2:]))
     offset += s.dtype.count
-  return UOp(Ops.NOOP, src=tuple(ret))
+  # dtype CAT
+  dtypes: list[PtrDType] = [x.dtype for x in ret if isinstance(x.dtype, PtrDType)]
+  assert len(dtypes) == len(ret) and all_same([(x.size, x.addrspace) for x in dtypes])
+  out_dtype = dtypes[0].base.scalar().vec(sum([x.count for x in dtypes])).ptr(dtypes[0].size, dtypes[0].addrspace)
+  return UOp(Ops.PTRCAT, dtype=out_dtype, src=tuple(ret))
 
 def gep_on_store(gep:UOp, st:UOp, sto:UOp):
   # NOTE: we need to invert the gep here, but it may be an expanding gep
@@ -122,8 +126,8 @@ def gep_on_store(gep:UOp, st:UOp, sto:UOp):
   return gep.src[0].store(st.gep(new_arg), *sto.src[2:])
 
 load_store_folding = PatternMatcher([
-  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat(GroupOp.Defines, name="buf")), UPat.var("vec"))), expand_index),
-  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat(GroupOp.Defines, name="buf")), UPat.var("vec"),
+  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL), name="buf")), UPat.var("vec"))), expand_index),
+  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL), name="buf")), UPat.var("vec"),
                         UPat.var("mask"))), expand_index),
   # GEP after LOAD
   (UPat(Ops.LOAD, src=(UPat(Ops.GEP, name="gep"),), name="ld", allow_any_len=True),
@@ -154,8 +158,6 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
     must_divide = False
   elif buf.dtype.base != dtypes.float and buf.dtype.base != dtypes.half and not isinstance(buf.dtype, ImageDType):
     pass
-  elif cast(PtrDType, buf.dtype).addrspace == AddrSpace.REG:
-    pass
   elif isinstance(buf.dtype, ImageDType):
     lengths = [4]
   elif ctx is not None and ctx.supports_float4:
@@ -182,8 +184,7 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
       break
 
   # if it wasn't split, we return None. otherwise we CAT them
-  if len(ret) <= 1: return None
-  return UOp(Ops.CAT, ls.dtype, tuple(ret)) if ls.op is Ops.LOAD else UOp(Ops.NOOP, src=tuple(ret))
+  return UOp(Ops.CAT, ls.dtype, tuple(ret)) if len(ret) > 1 else None
 
 def image_fixup(ls:UOp):
   # normal image load or store, with the CAST from expand_index
@@ -235,8 +236,9 @@ def no_vectorized_alu(alu:UOp):
 def no_vectorized_acc(acc:UOp, c:UOp):
   if acc.dtype.count == 1: return None
   assert c.arg == 0, "this only supports index 0"
-  new_acc = acc.replace(dtype=acc.dtype.base.scalar().ptr(acc.dtype.count, cast(PtrDType, acc.dtype).addrspace))
-  return UOp(Ops.PTRCAT, acc.dtype, tuple([new_acc.index(UOp.const(dtypes.int, i)) for i in range(acc.dtype.count)]))
+  alus = tuple(UOp(acc.op, acc.dtype.base.scalar().ptr(1, cast(PtrDType, acc.dtype).addrspace),
+    tuple(s.gep(i) if j == 0 else s for j,s in enumerate(acc.src)), acc.arg+(i,)).index(UOp.const(dtypes.int, 0)) for i in range(acc.dtype.count))
+  return UOp(Ops.PTRCAT, acc.dtype, alus)
 
 devectorize = PatternMatcher([
   # no ALU on vectorized dtypes
@@ -285,11 +287,10 @@ def reduce_to_acc(ctx:ReduceContext, red:UOp):
     input_ranges = tuple([x for x in inp.toposort(gate=lambda x: x.op is not Ops.STORE) if x.op is Ops.RANGE and x not in reduce_range])
     identity = red.const_like(identity_element(red.arg, red.dtype.scalar()))
     acc = UOp(Ops.DEFINE_REG, red.dtype.ptr(size=1, addrspace=AddrSpace.REG), arg=(ctx.acc_num,)).index(UOp.const(dtypes.int, 0))
-    do_store = acc.store(identity, UOp(Ops.NOOP, src=input_ranges)) if len(input_ranges) else acc.store(identity)
-    lst = [acc.load(do_store, *reduce_range)] + lst  # put acc as the first element
+    lst = [acc.store(identity, UOp(Ops.NOOP, src=input_ranges)).load(*reduce_range)] + lst  # put acc as the first element
     ctx.acc_num += 1
   ret = functools.reduce(lambda x,y: x.alu(red.arg, y), lst)
-  return acc.load(acc.store(ret, *reduce_range)) if len(reduce_range) != 0 else ret
+  return acc.store(ret, *reduce_range).load() if len(reduce_range) != 0 else ret
 
 def no_vectorized_reduce(inp:UOp, red:UOp):
   if inp.dtype != red.dtype:

tinygrad/codegen/expander.py

@@ -86,6 +86,9 @@ expander = PatternMatcher([
   (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.GEP, Ops.WMMA, Ops.LOAD, Ops.STORE, Ops.INDEX,
          Ops.VECTORIZE, Ops.IF, Ops.REDUCE), name="root", custom_early_reject=set([Ops.UNROLL])), do_expand),
   (UPat(Ops.CONTRACT, name="con"), do_contract),
+  # vectorize DEFINE_ACC
+  (UPat(Ops.VECTORIZE, src=UPat(Ops.DEFINE_REG, name="acc"), name="v"),
+    lambda acc,v: acc.replace(dtype=v.dtype, src=(acc.src[0].broadcast(v.dtype.count),)+acc.src[1:])),
   # BARRIERs aren't actually expanded
   (UPat(Ops.BARRIER, src=(UPat(Ops.UNROLL, name="ex"),)),
    lambda ex: UOp(Ops.UNROLL, src=(UOp(Ops.BARRIER, src=ex.src),)*len(ex.src), arg=ex.arg)),

tinygrad/codegen/gpudims.py

@@ -56,9 +56,8 @@ def add_gpudims(ctx:Renderer, s:UOp):
   if not ki.global_dims and not ki.local_dims: return None
   s_topo = list(s.toposort())
   if any(x.op is Ops.SPECIAL for x in s_topo): return None
-  all_ranges = {x.arg:x for x in s_topo if x.op is Ops.RANGE}
-  # NOTE: this supports globals/locals in any position
-  ranges = [all_ranges[r] for r in ki.global_dims+ki.local_dims]
+  ranges = sorted([x for x in s_topo if x.op is Ops.RANGE and x.arg in (ki.global_dims+ki.local_dims)], key=lambda x: x.arg)
+  if not len(ranges): return None
   global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg in ki.global_dims])
   local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg in ki.local_dims])
   if ki.dont_use_locals:

tinygrad/codegen/lowerer.py

@@ -2,34 +2,20 @@
 from dataclasses import dataclass
 from typing import cast
 from tinygrad.dtype import dtypes, PtrDType, AddrSpace
-from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType, graph_rewrite
+from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType
 from tinygrad.helpers import prod, partition, flatten
 
 # ***** indexing *****
 
 @dataclass
 class IndexContext:
-  axis_types: list[AxisType]
   idxs: list[UOp]
-  start: int = 0
-  #ridxs: list[UOp]
-
-def shape_to_idx(s, axis_types, start=0, allow_unroll=False):
-  idxs = []
-  for i, (s, at) in enumerate(zip(s, axis_types)):
-    if at in (AxisType.UPCAST, AxisType.UNROLL) and allow_unroll:
-      assert isinstance(s, int), "needs to be int to upcast/unroll"
-      idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s), tuple(range(s))),), ((i,s),)))
-    else:
-      # all others are RANGES
-      idxs.append(UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), start+i))
-  return idxs
+  ridxs: list[UOp]
 
 def get_index(ast:UOp) -> IndexContext:
   axis_types = ast.arg.axis_types if isinstance(ast.arg, KernelInfo) else ()
   if len(ast.full_shape) != len(axis_types): axis_types = (AxisType.LOOP,)*len(ast.full_shape)
 
-  """
   # indexes
   idxs = []
   for i, (s, at) in enumerate(zip(ast.full_shape, axis_types)):
@@ -45,85 +31,33 @@ def get_index(ast:UOp) -> IndexContext:
   for i, (s, at) in enumerate(zip(ast.full_shape, axis_types)):
     if at == AxisType.GROUP_REDUCE:
       ridxs[i] = UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), 1000+i)
-  """
 
-  return IndexContext(axis_types, []) #idxs, ridxs)
+  return IndexContext(idxs, ridxs)
 
 # ***** lowering (given index) *****
 
-def lower_reduce_into(ctx: IndexContext, x: UOp):
-  # x.src[1] is the big
-  axis_arg = [i for i,(s0,s1) in enumerate(zip(x.src[0].shape, x.src[1].shape)) if s0 != s1]
-  reduce_idxs = shape_to_idx([s for i,s in enumerate(x.src[1].shape) if i in axis_arg],
-                             [s for i,s in enumerate(ctx.axis_types) if i in axis_arg], ctx.start)
-  print("reduce into", [x.arg for x in reduce_idxs])
-  new_idxs = shape_to_idx(x.src[0].shape[:-len(reduce_idxs)], ctx.axis_types[:-len(reduce_idxs)], ctx.start+len(reduce_idxs)) + reduce_idxs
-
-  idx, valid = x.src[0].arg.to_indexed_uops(new_idxs)
-  used_idxs = [x for x in UOp.sink(idx, valid).toposort() if x in new_idxs]
-
-  real_new_idxs = []
-  for i in range(len(x.src[0].shape)):
-    if new_idxs[i] in used_idxs or new_idxs[i] in reduce_idxs or len(ctx.idxs) <= i: real_new_idxs.append(new_idxs[i])
-    else: real_new_idxs.append(ctx.idxs[i])
-  non_replaced = [x for x in real_new_idxs if x not in ctx.idxs]
-
-  acc = x.src[0].load(*reduce_idxs).alu(x.arg, x.src[1])
-
-  lc = IndexContext(ctx.axis_types, tuple(real_new_idxs), ctx.start+len(new_idxs))
-  from tinygrad.codegen.lowerer import pm_lowerer  # TODO: better way to do this?
-  ret = graph_rewrite(acc, pm_lowerer, lc, name="subreduce", bottom_up=True)
-  ctx.start = lc.start
-
-  red = x.src[0].src[0].index(idx, valid).store(ret, *used_idxs, *reduce_idxs)
-  return x.src[0].load(red)
-
 def lower_reduce_axis(ctx: IndexContext, x: UOp):
-  new_idx = shape_to_idx([s for i,s in enumerate(x.src[0].shape) if i in x.axis_arg],
-                         [s for i,s in enumerate(ctx.axis_types) if i in x.axis_arg], ctx.start)
-  full_new_idx = list(ctx.idxs)
-  for i,s in zip(x.axis_arg, new_idx):
-    #assert len(full_new_idx) == i, f"len(full_new_idx) = {len(full_new_idx)}, trying to place {i}"
-    #full_new_idx.append(s)
-    full_new_idx[i] = s
-
-  lc = IndexContext(ctx.axis_types, tuple(full_new_idx), ctx.start+len(new_idx))
-  from tinygrad.codegen.lowerer import pm_lowerer  # TODO: better way to do this?
-  ret = graph_rewrite(x.src[0], pm_lowerer, lc, name="subreduce", bottom_up=True)
-  ctx.start = lc.start
-
   # NOTE: always using ridxs is fine here
-  reduce_range, reduce_expand = partition([full_new_idx[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
+  reduce_range, reduce_expand = partition([ctx.ridxs[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
   assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
+  ret = x.src[0]
   if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
     ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis))
   # REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
   return UOp(Ops.REDUCE, x.dtype, (ret,)+tuple(reduce_range), x.arg[0])
 
 def lower_load(ctx: IndexContext, x: UOp, buf: UOp):
-  print("lower_load", [x.arg for x in ctx.idxs])
-  idx, valid = x.st_arg.to_indexed_uops(ctx.idxs)
-  return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + x.src[1:])
-
-  #barrier = tuple([y.barrier() if buf.op is Ops.DEFINE_LOCAL else y for y in x.src[1:]])
+  idx, valid = x.st_arg.to_indexed_uops(ctx.ridxs if buf.op is Ops.DEFINE_LOCAL else ctx.idxs)
+  barrier = (UOp(Ops.BARRIER, dtypes.void, (x.src[1],)),) if buf.op is Ops.DEFINE_LOCAL else ()
+  return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + barrier)
 
 def lower_store(ctx: IndexContext, x: UOp, buf: UOp):
-  assert x.src[1].shape == x.src[0].shape, f"shape mismatch on store {x.src[1].shape} != {x.src[0].shape}"
-  new_idxs = shape_to_idx(x.src[0].shape, ctx.axis_types, ctx.start)
-  idx, valid = x.st_arg.to_indexed_uops(new_idxs)
-  used_idxs = [x for x in UOp.sink(idx, valid).toposort() if x in new_idxs]
-  real_new_idxs = []
-  for i in range(len(x.src[0].shape)):
-    if new_idxs[i] in used_idxs or len(ctx.idxs) <= i:
-      real_new_idxs.append(new_idxs[i])
-    else:
-      real_new_idxs.append(ctx.idxs[i])
-  print("got", len(real_new_idxs), len(used_idxs))
-  lc = IndexContext(ctx.axis_types, tuple(real_new_idxs), ctx.start+len(new_idxs))
-  from tinygrad.codegen.lowerer import pm_lowerer  # TODO: better way to do this?
-  stored = graph_rewrite(x.src[1], pm_lowerer, lc, name="substore", bottom_up=True)
-  ctx.start = lc.start
-  return buf.index(idx, valid).store(stored, *[x for x in used_idxs if x.op is Ops.RANGE])
+  idx, valid = x.st_arg.to_indexed_uops(ctx.idxs)
+  if cast(PtrDType, buf.dtype).addrspace == AddrSpace.GLOBAL:
+    # NOTE: only store the local reduceop in the threads that are actually doing the reduce
+    for oidx, ridx in zip(ctx.idxs, ctx.ridxs):
+      if oidx is not ridx: valid = valid * oidx.eq(0)
+  return buf.index(idx, valid).store(x.src[1], *[x for x in UOp.sink(idx, valid).toposort() if x.op is Ops.RANGE])
 
 def lower_const(ctx:IndexContext, view:UOp, c:UOp):
   if all(x.mask is None for x in view.arg.views): return c
@@ -138,7 +72,6 @@ pm_lowerer = PatternMatcher([
   (UPat(Ops.VALID, src=(UPat(Ops.VIEW, name="v"),)).where(UPat.cvar("c"), UPat(Ops.CONST, arg=0)), lambda c,v: c.replace(src=()).view(v.arg)),
 
   # reduce/view_const
-  (UPat(Ops.REDUCE_INTO, name="x"), lower_reduce_into),
   (UPat(Ops.REDUCE_AXIS, name="x"), lower_reduce_axis),
   (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="c"),), name="view"), lower_const),
   # rewrite LOAD/STORE VIEW to LOAD/STORE with indexed

tinygrad/device.py

@@ -336,7 +336,7 @@ if PROFILE:
 
     if not getenv("SQTT", 0):
       from tinygrad.uop.ops import launch_viz
-      launch_viz(PROFILE, fn)
+      launch_viz("PROFILE", fn)
 
 if __name__ == "__main__":
   for device in ALL_DEVICES:

tinygrad/engine/realize.py

@@ -13,7 +13,7 @@ from tinygrad.uop.spec import type_verify
 
 # **************** Program Creation ****************
 
-@track_rewrites(name=lambda _ast,_renderer,ret: TracingKey(ret.name, (ret.function_name, ret.ast), ret.src, ret=ret))
+@track_rewrites(name=lambda _ast,_renderer,ret: TracingKey(ret.name, (ret.function_name, ret.ast), ret.src))
 def get_program(ast:UOp, renderer:Renderer) -> ProgramSpec:
   """
   Transform an AST into a ProgramSpec. May trigger BEAM search.

tinygrad/helpers.py

@@ -191,7 +191,6 @@ class TracingKey:
   keys:tuple[str, ...]=()                # optional keys to search for related traces
   fmt:str|None=None                      # optional detailed formatting
   cat:str|None=None                      # optional category to color this by
-  ret:Any=None
 
 class ProfileEvent: pass
 

tinygrad/opt/kernel.py

@@ -76,6 +76,9 @@ class Kernel:
     full_shape = ast.full_shape
     self.sts.append(ShapeTracker.from_shape(full_shape, (0,)*len(full_shape)))
 
+    # extend all shapes of all shapetrackers
+    self.sts = [x.reshape(x.shape+(1,)*(len(full_shape)-len(x.shape))) for x in self.sts]
+
     # parameters for optimization
     self.tensor_core: TensorCore|None = None
     self.tensor_core_opts: TensorCoreOptions|None = None
@@ -448,6 +451,8 @@ class Kernel:
       ret = op.replace(src=tuple(fixup_ast(x) for x in op.src)) # noqa: F821
       if op.op in GroupOp.Buffer and op in self.bufs:
         st = self.sts[self.bufs.index(op)]
+        # late remove all ones
+        st = st.reshape(tuple([x for x in st.shape if resolve(x != 1)]))
         # NOTE: if CONST got masked after applying opts, we create a new VALID
         if op.op is Ops.CONST and any(v.mask is not None for v in st.views): return op.view(st).valid()
         # otherwise we just replace the VIEW source

tinygrad/renderer/cstyle.py

@@ -75,10 +75,6 @@ extra_pm = PatternMatcher([
 
 def uops_to_dtypes(uops:list[UOp]) -> list[DType]: return dedup(u.dtype for u in uops if not isinstance(u.dtype, (ImageDType, PtrDType)))
 
-# (name, dims, dtype_in, dtype_out, device, threads, upcast_axes, reduce_axes)
-def wmma_args(uops:list[UOp]):
-  return dedup((uop.arg[0], uop.arg[1], uop.src[0].dtype.scalar(), uop.dtype.scalar(), *(uop.arg[4:8])) for uop in uops if uop.op is Ops.WMMA)
-
 class CStyleLanguage(Renderer):
   kernel_typedef: str = "void"
   buffer_prefix: str = ""
@@ -122,9 +118,7 @@ class CStyleLanguage(Renderer):
   def render_dtype(self, dt:DType, mutable=True) -> str:
     if isinstance(dt, ImageDType): return f"{'write_only' if mutable else 'read_only'} image2d_t"
     if isinstance(dt, PtrDType):
-      prefix = ""
-      if dt.addrspace == AddrSpace.LOCAL and self.smem_prefix_for_cast: prefix = self.smem_prefix
-      if dt.addrspace == AddrSpace.GLOBAL: prefix = self.buffer_prefix
+      prefix = self.smem_prefix if dt.addrspace == AddrSpace.LOCAL and self.smem_prefix_for_cast else self.buffer_prefix
       return prefix + self.render_dtype(dt.base) + "*"
     if dt.count > 1: return self.type_map.get(scalar:=dt.scalar(), scalar.name).replace(" ", "_") + str(dt.count)
     return self.type_map.get(scalar:=dt.scalar(), scalar.name)
@@ -173,8 +167,10 @@ class CStyleLanguage(Renderer):
         (u.op in {Ops.VECTORIZE, *(GroupOp.ALU-{Ops.WHERE}), Ops.CAST, Ops.BITCAST} and child_count[u] == 1 and not getenv("EXPAND_SSA"))):
         r[u] = l
       else:
-        if u.op in {Ops.RANGE, Ops.DEFINE_LOCAL, Ops.STORE, Ops.DEFINE_REG} or u.dtype == dtypes.void: pass
-        else: l = f"{self.render_dtype(u.dtype)} {r[u]} = {l}" + (";" if u.op is not Ops.SPECIAL else "")
+        if u.op in {Ops.RANGE, Ops.DEFINE_LOCAL, Ops.STORE, Ops.DEFINE_REG} or u.dtype == dtypes.void:
+          if u.op is Ops.STORE: r[u] = r[u.src[0]]
+        else:
+          l = f"{self.render_dtype(u.dtype)} {r[u]} = {l}" + (";" if u.op is not Ops.SPECIAL else "")
         kernel.append("  "*depth + l)
         if prefix: c[prefix] += 1  # if it was used, increment
       if u.op in {Ops.IF, Ops.RANGE}: depth += 1
@@ -214,7 +210,7 @@ class ClangRenderer(CStyleLanguage):
   def _render_defines(self, uops) -> list[str]:
     prefix = [self.render_vector_prefix(dt) for dt in uops_to_dtypes(uops) if dt.count > 1]
     # https://github.com/corsix/amx
-    for name, (N, M, _), dtype_in, _, _, _, _, _ in wmma_args(uops):
+    for name, (N, M, _), dtype_in, _, _, _, _, _ in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
       prefix += [
         '#define AMX_SET(imm5) __asm("nop\\nnop\\nnop\\n.word (0x201000+(%0<<5)+%1)" : : "i"(17), "i"(imm5) : "memory")',
         '#define AMX(op, gpr, btf) __asm(".word (0x201000+(%0 << 5)+0%1-((0%1>>4)*6))" : : "i"(op), "r"((unsigned long long)(gpr)+(btf)) : "memory")',
@@ -274,9 +270,9 @@ class IntelRenderer(OpenCLRenderer):
 
   def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
     prefix = []
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops):
-      dt_in = ("ushort", "bf16") if dtype_in == dtypes.bfloat16 else (dtype_in.name, "f16")
-      prefix.append(f"""{dtype_out.name}8 __{name}({dt_in[0]}16 a, {dt_in[0]}16 b, {dtype_out.name}8 c) {{
+    for arg in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
+      dt_in = ("ushort", "bf16") if arg[2] == dtypes.bfloat16 else (arg[2].name, "f16")
+      prefix.append(f"""{arg[3].name}8 __{arg[0]}({dt_in[0]}16 a, {dt_in[0]}16 b, {arg[3].name}8 c) {{
     return intel_sub_group_{dt_in[1]}_{dt_in[1]}_matrix_mad_k16(as_int8(a), as_int8(b), c);\n}}""")
     return super().render_kernel(function_name, kernel, bufs, uops, prefix or None)
 
@@ -312,13 +308,13 @@ class MetalRenderer(CStyleLanguage):
   ]) + base_rewrite
 
   def render_kernel(self, function_name, kernel, bufs, uops, prefix=None):
-    prefix = ["#include <metal_stdlib>","using namespace metal;"]
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops): prefix.append(
-  f"""{(dstr_out:=self.render_dtype(dtype_out.vec(2)))} __{name}({(dstr_in:=self.render_dtype(dtype_in.vec(2)))} a, {dstr_in} b, {dstr_out} c){{
-  simdgroup_{self.render_dtype(dtype_in)}8x8 mat_a, mat_b; simdgroup_{self.render_dtype(dtype_out)}8x8 mat_c;
+    prefix, wmma_args = ["#include <metal_stdlib>","using namespace metal;"], set([uop.arg for uop in uops if uop.op is Ops.WMMA])
+    for arg in wmma_args: prefix.append(
+  f"""{(dtype_out:=self.render_dtype(arg[3].vec(2)))} __{arg[0]}({(dtype_in:=self.render_dtype(arg[2].vec(2)))} a, {dtype_in} b, {dtype_out} c){{
+  simdgroup_{self.render_dtype(arg[2])}8x8 mat_a, mat_b; simdgroup_{self.render_dtype(arg[3])}8x8 mat_c;
   mat_a.thread_elements()[0] = a[0]; mat_b.thread_elements()[0] = b[0]; mat_c.thread_elements()[0] = c[0];
   mat_a.thread_elements()[1] = a[1]; mat_b.thread_elements()[1] = b[1]; mat_c.thread_elements()[1] = c[1];
-  simdgroup_multiply_accumulate(mat_c, mat_a, mat_b, mat_c);\n  return {dstr_out}(mat_c.thread_elements()[0], mat_c.thread_elements()[1]);\n}}""")
+  simdgroup_multiply_accumulate(mat_c, mat_a, mat_b, mat_c);\n  return {dtype_out}(mat_c.thread_elements()[0], mat_c.thread_elements()[1]);\n}}""")
     return super().render_kernel(function_name, kernel, bufs, uops, prefix)
 
 _nms = "xyzwabcdefghijkl"
@@ -367,7 +363,7 @@ class CUDARenderer(CStyleLanguage):
 
     dt_map_in = { dtypes.float: "tf32", dtypes.half: "f16", dtypes.bfloat16: "bf16" }
     dt_map_out = { dtypes.float: "f32", dtypes.half: "f16" }
-    for name, (N, M, K), dtype_in, dtype_out, _, _, upcast_axes, _ in wmma_args(uops):
+    for name, (N, M, K), dtype_in, dtype_out, _, _, upcast_axes, _ in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
       upcast_sizes = [prod(size for _, size in upcast) for upcast in upcast_axes]
       wmma_dtypes = [self.render_dtype(dtype.vec(size)) for dtype, size in zip([dtype_in, dtype_in, dtype_out], upcast_sizes)]
       n_operands = [size*dtype.itemsize//4 for dtype, size in zip([dtype_in, dtype_in, dtype_out], upcast_sizes)] # 4 => CUDA reg size in bytes
@@ -462,15 +458,15 @@ class AMDRenderer(CStyleLanguage):
     if any(dt.scalar() == dtypes.bfloat16 for dt in used_dtypes): prefix.append("typedef unsigned short hip_bfloat16;")
     prefix += [self.render_vector_prefix(dt) for dt in used_dtypes if dt.count > 1]
 
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops): # TODO: handle TCs f32_bf16 and bf16_bf16 w/ wrapper
+    for arg in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]): # TODO: handle TCs f32_bf16 and bf16_bf16 w/ wrapper
       if self.tensor_cores == tc.amd_cdna:
-        prefix.append(f"#define __{name} __builtin_amdgcn_mfma_f32_16x16x16{'f16' if dtype_in == dtypes.half else 'bf16_1k'}")
+        prefix.append(f"#define __{arg[0]} __builtin_amdgcn_mfma_f32_16x16x16{'f16' if arg[2] == dtypes.half else 'bf16_1k'}")
       # #define __WMMA_16_16_16_half_half __builtin_amdgcn_wmma_f16_16x16x16_f16_w32_gfx12
       elif self.tensor_cores == tc.amd_rdna4:
-        prefix.append(f"#define __{name} __builtin_amdgcn_wmma_{type_map[dtype_out]}_16x16x16_{type_map[dtype_in]}_w32_gfx12")
-      elif dtype_out == dtypes.float:
-        prefix.append(f"#define __{name} __builtin_amdgcn_wmma_f32_16x16x16_{'f16' if dtype_in == dtypes.half else 'bf16'}_w32")
-      else: prefix.append(f"static inline __attribute__((device)) half8 __{name}"+"""(half16 a, half16 b, half8 c) {
+        prefix.append(f"#define __{arg[0]} __builtin_amdgcn_wmma_{type_map[arg[3]]}_16x16x16_{type_map[arg[2]]}_w32_gfx12")
+      elif arg[3] == dtypes.float:
+        prefix.append(f"#define __{arg[0]} __builtin_amdgcn_wmma_f32_16x16x16_{'f16' if arg[2] == dtypes.half else 'bf16'}_w32")
+      else: prefix.append(f"static inline __attribute__((device)) half8 __{arg[0]}"+"""(half16 a, half16 b, half8 c) {
   half16 c_frag = {}; half8 d; for (int n = 0; n < 8; n++) { c_frag[n*2] = c[n]; }
   c_frag = __builtin_amdgcn_wmma_f16_16x16x16_f16_w32(a, b, c_frag, false);
   for (int n = 0; n < 8; n++) { d[n] = c_frag[n*2]; } return d;\n}""")

tinygrad/renderer/llvmir.py

@@ -188,6 +188,9 @@ class LLVMRenderer(Renderer):
         if (l:=self.string_rewrite.rewrite(u, ctx=r)) is None:
           raise RuntimeError(f"failed to render {u.op} with {u.dtype} srcs {[x.dtype for x in u.src]}")
         kernel.append(cast(str, l))
+
+        # stores pass the first arg through
+        if u.op is Ops.STORE: r[u] = r[u.src[0]]
     return tuple(local_args), self._render_fn(name, args, kernel, prefix)
 
 barrier = 'fence syncscope("workgroup") release\ntail call void @llvm.amdgcn.s.barrier()\nfence syncscope("workgroup") acquire\n'

tinygrad/renderer/ptx.py

@@ -186,18 +186,11 @@ class PTXRenderer(Renderer):
       if u.op in {Ops.CAST, Ops.BITCAST} and (u.src[0].dtype == u.dtype or isinstance(u.src[0].dtype, PtrDType)):
         r[u] = r[u.src[0]]
         continue
-      if u.op is Ops.DEFINE_REG:
-        r[u] = [ssa("reg", u, self.types[u.dtype.base.scalar()]) for _ in range(cast(PtrDType, u.dtype).size)]
-        continue
       if u.op in {Ops.INDEX, Ops.LOAD, Ops.STORE} and isinstance(u.src[0].dtype, PtrDType) and u.src[0].dtype.addrspace == AddrSpace.REG:
-        if u.op is Ops.INDEX:
-          assert u.src[1].op == Ops.CONST, f"index on REG in ptx only supported on CONST, not {u.src[1].op}"
-          r[u] = r[u.src[0]][u.src[1].arg]
-        else:
-          r[u] = r[u.src[0]]
-          if u.op is Ops.STORE:
-            typ = "pred" if u.src[1].dtype == dtypes.bool else ("b"+self.types[u.src[1].dtype][1:])
-            kernel.append(f"mov.{typ} {self.r[u.src[0]]}, {self.r[u.src[1]]};")
+        r[u] = r[u.src[0]]
+        if u.op is Ops.STORE:
+          typ = "pred" if u.src[1].dtype == dtypes.bool else ("b"+self.types[u.src[1].dtype][1:])
+          kernel.append(f"mov.{typ} {self.r[u.src[0]]}, {self.r[u.src[1]]};")
         continue
       if u.op is Ops.SPECIAL: r[u] = "%" + u.arg[0]
       elif u.op is Ops.DEFINE_VAR: bufs.append((u.arg[0], u.dtype))
@@ -207,12 +200,12 @@ class PTXRenderer(Renderer):
       elif u.op is Ops.DEFINE_GLOBAL: bufs.append((f"data{u.arg}", u.dtype))
       elif u.op is Ops.WMMA:
         # registers for packing/unpacking input and acc
-        self.wmma_r = [[ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[0]]), 4 // u.src[0].dtype.scalar().itemsize)],
-                       [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.src[0].dtype.scalar().itemsize)],
-                       [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.dtype.scalar().itemsize)]]
+        self.wmma_r = [[ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[0]]), 4 // u.arg[2].itemsize)],
+                       [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.arg[2].itemsize)],
+                       [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.arg[3].itemsize)]]
         r[u] = [ssa("wmma", dtype=self.types[u.dtype.scalar()]) for _ in range(u.dtype.count)]
       prefix, dtype = {Ops.CAST: ("cast", None), Ops.BITCAST: ("cast", None), Ops.ENDRANGE: ("pred", "pred"), Ops.RANGE: ("ridx", None),
-        Ops.DEFINE_VAR: ("dat", None), Ops.CONST: ("const", None), Ops.DEFINE_LOCAL:("local",self.types[dtypes.ulong]),
+        Ops.DEFINE_REG: ("acc", None), Ops.DEFINE_VAR: ("dat", None), Ops.CONST: ("const", None), Ops.DEFINE_LOCAL:("local",self.types[dtypes.ulong]),
         Ops.DEFINE_GLOBAL: ("dat", self.types[dtypes.ulong]), **{op: ("alu", None) for op in GroupOp.ALU}}.get(u.op, (None, None))
       if prefix: r[u] = ssa(prefix, u, dtype)
 

tinygrad/runtime/ops_metal.py

@@ -1,6 +1,6 @@
-import subprocess, pathlib, struct, ctypes, tempfile, functools, contextlib, decimal, platform
+import os, pathlib, struct, ctypes, tempfile, functools, contextlib, decimal, platform
 from typing import Any, cast
-from tinygrad.helpers import prod, to_mv, getenv, round_up, cache_dir, T, init_c_struct_t, PROFILE, ProfileRangeEvent, cpu_profile, unwrap
+from tinygrad.helpers import prod, to_mv, getenv, round_up, cache_dir, T, init_c_struct_t, PROFILE, ProfileRangeEvent, cpu_profile
 from tinygrad.device import Compiled, Compiler, CompileError, LRUAllocator, ProfileDeviceEvent
 from tinygrad.renderer.cstyle import MetalRenderer
 
@@ -144,10 +144,7 @@ class MetalCompiler(Compiler):
     with tempfile.NamedTemporaryFile(delete=True) as shader:
       shader.write(lib)
       shader.flush()
-      proc = subprocess.Popen(f"cd {pathlib.Path(__file__).parents[2]}/extra/disassemblers/applegpu && python3 compiler_explorer.py {shader.name}",
-                              stdout=subprocess.PIPE, shell=True, text=True, bufsize=1)
-      for line in unwrap(proc.stdout): print(line, end="")
-      ret = proc.wait()
+      ret = os.system(f"cd {pathlib.Path(__file__).parents[2]}/extra/disassemblers/applegpu && python3 compiler_explorer.py {shader.name}")
       if ret: print("Disassembler Error: Make sure you have https://github.com/dougallj/applegpu cloned to tinygrad/extra/disassemblers/applegpu")
 
 class MetalProgram:

tinygrad/runtime/ops_python.py

@@ -40,7 +40,7 @@ class PythonProgram:
       loop_ends: dict[int, int] = {}
       while i < len(self.uops):
         uop, dtype, idp, arg = self.uops[i]
-        void_ops = {Ops.ENDRANGE, Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK, Ops.NOOP, Ops.STORE}
+        void_ops = {Ops.ENDRANGE, Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK, Ops.NOOP}
         inp = [ul[v] for v in idp if self.uops[v][0] not in void_ops]
         dtp = [dl[v] for v in idp if self.uops[v][0] not in void_ops]
         if getenv("TRACE"): print(i, uop, dtype, arg, inp, dtp)
@@ -58,6 +58,7 @@ class PythonProgram:
           for j,val in enumerate(inp[1] if dtp[1].count > 1 else [inp[1]]):
             for (m,o,g),v in zip(inp[0], val):
               if g: _store(m, o+j, v)
+          ul[i] = inp[0]
           i += 1
           continue
         if uop in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
@@ -123,27 +124,24 @@ class PythonProgram:
                   out[elem_idx][goff+lane_id] += sum(a_elem(inp[0], _k, c_j, goff) * b_elem(inp[1], c_i, _k, goff) for _k in range(K))
             return out
 
-          first_src_dtype = self.uops[idp[0]][1]
-          assert isinstance(first_src_dtype, DType) # mypy
-          dims, dtype_in, device, threads = arg[1], first_src_dtype.scalar(), arg[4], arg[5]
           # TODO: refactor these to a shared TensorCoreLayout in kernel.py
-          if device == "METAL":
+          if arg[4] == "METAL":
             # A (2 elements on 32 threads): row major
             def a_b_elem(x, i, j, goff): return x[(i%2)][goff+(i//2)%2+(j%4)*2+(i//4)*8+(j//4)*16]
             # (i, j), C, D (2 elements on 32 threads): row major same as A/B
             def c_map(lane, elem): return (elem + ((lane%2)*2) + ((lane//8)%2)*4, ((lane//2)%4) + (lane//16)*4)
             ul[i] = wmma_helper(32, 8, 2, 2, 2, a_b_elem, a_b_elem, c_map)
-          elif device == "AMD" and threads == 64:
+          elif arg[4] == "AMD" and arg[5] == 64:
             def a_elem(x, k, row, goff): return x[k%4][goff + (k//4)*16 + row]
             def b_elem(x, col, k, goff): return a_elem(x, k, col, goff) # pylint: disable=arguments-out-of-order
             def c_map(lane, elem): return (lane%16, (lane//16)*4 + elem)
             ul[i] = wmma_helper(64, 16, 4, 4, 4, a_elem, b_elem, c_map)
-          elif device == "AMD" and len(inp[0]) == 8: # RDNA4
+          elif arg[4] == "AMD" and len(inp[0]) == 8: # RDNA4
             def a_elem(x, k, row, goff): return x[k - [0, 4, 4, 8][k//4]][goff + row + [0, 16, 0, 16][k//4]]
             def b_elem(x, col, k, goff): return a_elem(x, k, col, goff)
             def c_map(lane, elem): return (lane%16, (lane//16)*8 + elem)
             ul[i] = wmma_helper(32, 16, 8, 8, 8, a_elem, b_elem, c_map)
-          elif device == "AMD":
+          elif arg[4] == "AMD":
             # A (16 elements on 32 threads): col major, lane 16-32 == lane 0-15
             def a_elem(x, k, row, goff):
               assert x[k][goff+row] == x[k][goff+row+16], "warp elements not duplicated properly across lanes"
@@ -152,27 +150,27 @@ class PythonProgram:
             def b_elem(x, col, k, goff): return a_elem(x, k, col, goff)  # pylint: disable=arguments-out-of-order
             def c_map(lane, elem): return (lane%16, lane//16+elem*2) # (i, j), C, D (8 elements on 32 threads): row major
             ul[i] = wmma_helper(32, 16, 16, 16, 8, a_elem, b_elem, c_map)
-          elif device == "CUDA":
+          elif arg[4] == "CUDA":
             # (col, row) given (lane, elem) for C & D (4 elements on 32 threads); shared by all tc shapes with M=16 N=8
             def c_map(lane, elem): return (elem%2 + (lane%4)*2, lane//4 + (elem//2)*8)
 
-            if dims == (8,16,16):
+            if arg[1] == (8,16,16):
               def a_elem(x, k, row, goff): return x[k%2 + (row//8)*2 + (k//8)*4][goff + (k//2)%4 + (row%8)*4]
               def b_elem(x, col, k, goff): return x[k%2 + (k//8)*2][goff + (k//2)%4 + col*4]
               ul[i] = wmma_helper(32, 16, 8, 4, 4, a_elem, b_elem, c_map)
 
-            elif dims == (8,16,8) and dtype_in == dtypes.half:
+            elif arg[1] == (8,16,8) and arg[2] == dtypes.half:
               def a_elem(x, k, row, goff): return x[k%2 + (row//8)*2][goff + k//2 + (row%8)*4]
               def b_elem(x, col, k, goff): return x[k%2][goff + k//2 + col*4]
               ul[i] = wmma_helper(32, 8, 4, 2, 4, a_elem, b_elem, c_map)
 
-            elif dims == (8,16,8) and dtype_in == dtypes.float:
+            elif arg[1] == (8,16,8) and arg[2] == dtypes.float:
               def a_elem(x, k, row, goff): return x[(k//4)*2 + row//8][goff + k%4 + (row%8)*4]
               def b_elem(x, col, k, goff): return x[k//4][goff + k%4 + col*4]
               ul[i] = wmma_helper(32, 8, 4, 2, 4, a_elem, b_elem, c_map)
 
             else: raise NotImplementedError(f"unimplemented tensor core {arg}")
-          elif device == "INTEL":
+          elif arg[4] == "INTEL":
             # A (16 elements on 8 threads)
             def a_elem(x, k, row, goff): return x[k%2+row*2][goff+k//2]
             # B (16 elements on 8 threads)
@@ -180,7 +178,7 @@ class PythonProgram:
             # C, D (8 elements on 8 threads)
             def c_map(lane, elem): return (lane, elem)
             ul[i] = wmma_helper(8, 16, 16, 16, 8, a_elem, b_elem, c_map)
-          elif device == "CPU":
+          elif arg[4] == "CPU":
             def elem(x, col, row, _): return x[col+row][0] # k is always 0
             def c_map(_, elem): return (elem%16, elem//16)
             ul[i] = wmma_helper(1, 1, 16, 16, 256, elem, elem, c_map)

tinygrad/runtime/support/hcq.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 from typing import cast, Callable, Type, TypeVar, Generic, Any
 import contextlib, decimal, statistics, time, ctypes, array, os, struct, traceback, collections
-try: import fcntl # windows misses that
-except ImportError: fcntl = None #type:ignore[assignment]
 from tinygrad.helpers import PROFILE, getenv, to_mv, round_up, ProfileRangeEvent
 from tinygrad.renderer import Renderer
 from tinygrad.device import BufferSpec, Compiler, Compiled, LRUAllocator, ProfileDeviceEvent, ProfileProgramEvent
@@ -27,7 +25,9 @@ class FileIOInterface:
     self.fd:int = fd or os.open(path, flags)
   def __del__(self):
     if hasattr(self, 'fd'): os.close(self.fd)
-  def ioctl(self, request, arg): return fcntl.ioctl(self.fd, request, arg)
+  def ioctl(self, request, arg):
+    import fcntl # to support windows
+    return fcntl.ioctl(self.fd, request, arg)
   def mmap(self, start, sz, prot, flags, offset):
     x = libc.mmap(start, sz, prot, flags, self.fd, offset)
     if x == 0xffffffffffffffff: raise OSError(f"Failed to mmap {sz} bytes at {hex(start)}: {os.strerror(ctypes.get_errno())}")

tinygrad/schedule/kernelize.py

@@ -191,7 +191,7 @@ view_left = merge_views+PatternMatcher([
   (UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID}, name="e"),), name="view"),
    lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
   # if there's ones added after reduce, put this before the reduce
-  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
+  #(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
 ])
 
 def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")

tinygrad/shape/shapetracker.py

@@ -84,7 +84,7 @@ class ShapeTracker:
   @property
   def size(self) -> int: return self.views[-1].size()
 
-  def reduce(self, axis:tuple[int, ...]) -> tuple[sint, ...]: return tuple(1 if i in axis else s for i,s in enumerate(self.shape))
+  def reduce(self, axis:tuple[int, ...]) -> tuple[sint, ...]: return tuple(s for i,s in enumerate(self.shape) if i not in axis)
 
   def to_uop(self) -> UOp: return UOp(Ops.VIEW, dtypes.void, (), self)
   def to_indexed_uops(self, _idxs:list[UOp]|tuple[UOp, ...]|None=None) -> tuple[UOp, UOp]:

tinygrad/uop/__init__.py

@@ -40,7 +40,7 @@ class Ops(FastEnum):
   SPECIAL = auto()
 
   # reduce
-  REDUCE_AXIS = auto(); REDUCE_INTO = auto(); REDUCE = auto(); ALLREDUCE = auto() # noqa: E702
+  REDUCE_AXIS = auto(); REDUCE = auto(); ALLREDUCE = auto() # noqa: E702
 
   # optimization helper ops
   UNROLL = auto(); CONTRACT = auto(); GEP = auto(); VECTORIZE = auto(); CAT = auto(); PTRCAT = auto() # noqa: E702
@@ -83,8 +83,6 @@ class GroupOp:
   Ternary = {Ops.WHERE, Ops.MULACC}
   ALU = set.union(Unary, Binary, Ternary)
 
-  Defines = {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}
-
   Irreducible = {Ops.CONST, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.RANGE}
   Movement = {Ops.RESHAPE, Ops.EXPAND, Ops.PERMUTE, Ops.PAD, Ops.SHRINK, Ops.FLIP}
 

tinygrad/uop/ops.py

@@ -138,7 +138,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def st(self) -> ShapeTracker|None:
     if self.op in GroupOp.Block or self.op is Ops.INDEX: return None
     from tinygrad.shape.shapetracker import ShapeTracker
-    if self.op is Ops.REDUCE_INTO: return self.src[0].st
     # VIEW and MovementOps define a new ShapeTracker from the arg
     if self.op is Ops.VIEW: return self.arg
     if self.op in GroupOp.Movement: return unwrap(self.src[0].st).mop(self.op, self.arg)
@@ -237,9 +236,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       i = (i,)
     return UOp(Ops.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)
   def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, dtype=kwargs.pop("dtype", self.dtype.base), src=(self,)+src, **kwargs)
-  def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, dtypes.void, (self,)+src, **kwargs)
+  def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, self.dtype, (self,)+src, **kwargs)
   def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))
-  def barrier(self): return UOp(Ops.BARRIER, src=(self,))
   def alu(self, op, *src:UOp, **kwargs):
     out_dtype = (self, *src)[-1].dtype
     if op in {Ops.CMPLT, Ops.CMPNE}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
@@ -258,12 +256,12 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def valid(self): return UOp.where(UOp(Ops.VALID, dtypes.bool, (UOp(Ops.VIEW, arg=self.st),)), self.const_like(self.base.arg), 0)
   @staticmethod
   def range(dtype:DType, end:sint, idx:int): return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end),), arg=idx)
-  def r(self, op:Ops, axis:tuple[int, ...]):
+  def r(self, op:Ops, axis:tuple[int, ...], permute=True):
     axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
     if len(axis) == 0: return self
     # move any non reduce axis before the first reduce axis
     move_early, rest = partition(range(axis[0], len(self.shape)), lambda i: i not in axis and resolve(self.shape[i] != 1))
-    if move_early:
+    if move_early and permute:
       permaxis = tuple(range(axis[0])) + tuple(move_early) + tuple(rest)
       ret = self.permute(permaxis)
       new_axis = tuple([x for x in range(axis[0]+len(move_early), len(self.shape)) if resolve(ret.shape[x] != 1)])
@@ -860,7 +858,7 @@ if TRACK_MATCH_STATS or PROFILE:
       with open(fn:=temp("rewrites.pkl", append_user=True), "wb") as f:
         print(f"rewrote {len(tracked_ctxs)} graphs and matched {sum(len(r.matches) for x in tracked_ctxs for r in x)} times, saved to {fn}")
         pickle.dump((tracked_keys, tracked_ctxs, uop_fields), f)
-    if VIZ: launch_viz(VIZ, temp("rewrites.pkl", append_user=True))
+    if VIZ: launch_viz("VIZ", temp("rewrites.pkl", append_user=True))
     if getenv("PRINT_MATCH_STATS", TRACK_MATCH_STATS.value):
       ret = [0,0,0.0,0.0]
       for k,v in sorted(list(match_stats.items()), key=lambda x: x[1][2]+x[1][3]):
@@ -870,10 +868,9 @@ if TRACK_MATCH_STATS or PROFILE:
       print(f"{ret[0]:6d} / {ret[1]:7d} -- {ret[3]*1000.:9.2f} / {(ret[2]+ret[3])*1000.:9.2f} ms -- TOTAL")
       print(f"{len(match_stats)} rules, {sum(v[0] > 0 for v in match_stats.values())} matched once")
 
-  def launch_viz(var:ContextVar, data:str):
-    os.environ[(env_str:=var.key)] = "0"
+  def launch_viz(env_str:str, data:str):
+    os.environ[env_str] = "0"
     os.environ[f"{env_str}_DATA"] = data
-    os.environ[f"{env_str}_VALUE"] = str(var.value)
     if not int(os.getenv("VIZ", "0")) and not int(os.getenv("PROFILE", "0")):
       args = ['--kernels', getenv("VIZ_DATA", "")] if getenv("VIZ_DATA", "") else []
       args += ['--profile', getenv("PROFILE_DATA", "")] if getenv("PROFILE_DATA", "") else []

tinygrad/uop/spec.py

@@ -144,18 +144,18 @@ spec = PatternMatcher([
   (UPat(Ops.CONST, name="x"), lambda x: type(x.arg) is type(dtypes.as_const(x.arg, x.dtype))),
 
   # early LOAD has a <bufview, store?>
-  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.Defines),)),)), lambda: True),
-  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.Defines),)), UPat(Ops.STORE))), lambda: True),
+  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL)),)),)), lambda: True),
+  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL)),)), UPat(Ops.STORE))), lambda: True),
 
   # early STORE has a <bufview, val>
-  (UPat(Ops.STORE, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.Defines),)), UPat())), lambda: True),
+  (UPat(Ops.STORE, src=(UPat(Ops.VIEW, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL)),)), UPat())), lambda: True),
 
   # **** new style load/store ****
 
   # INDEX is used in new style load/store
   # INDEX takes a <buf, alu, gate?>
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat())), lambda: True),
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
+  (UPat(Ops.INDEX, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG)), UPat())), lambda: True),
+  (UPat(Ops.INDEX, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG)), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
 
   # LOAD on STORE
   (UPat(Ops.LOAD, src=(UPat(Ops.STORE),), allow_any_len=True), lambda: True),
@@ -165,8 +165,8 @@ spec = PatternMatcher([
   (UPat(Ops.LOAD, src=(index_pat,), allow_any_len=True), validate_index),
 
   # STORE takes a <bufidx, val, gate?>
-  (UPat(Ops.STORE, dtype=dtypes.void, src=(index_pat, UPat(name="val"), UPat(Ops.IF, name="gate")), allow_any_len=True), validate_store),
-  (UPat(Ops.STORE, dtype=dtypes.void, src=(index_pat, UPat(name="val")), allow_any_len=True), validate_store),
+  (UPat(Ops.STORE, src=(index_pat, UPat(name="val"), UPat(Ops.IF, name="gate")), allow_any_len=True), validate_store),
+  (UPat(Ops.STORE, src=(index_pat, UPat(name="val")), allow_any_len=True), validate_store),
 
   # most ALUs have all matching dtypes, except CMPLT, CMPNE, and WHERE
   (UPat(Ops.WHERE, name="w", src=(UPat(dtype=dtypes.bool), UPat.var("x"), UPat.var("y"))), lambda w,x,y: w.dtype == x.dtype == y.dtype),
@@ -198,7 +198,7 @@ spec = PatternMatcher([
   # NOTE: for testing, we let sinks be anything
   #(UPat(Ops.SINK, src=UPat(Ops.STORE)), lambda: True),
   (UPat(Ops.SINK, dtypes.void), lambda: True),
-  (UPat((Ops.NOOP, Ops.CUSTOMI, Ops.CUSTOM, Ops.PRECAST, Ops.REDUCE_INTO)), lambda: True),
+  (UPat((Ops.NOOP, Ops.CUSTOMI, Ops.CUSTOM, Ops.PRECAST)), lambda: True),
 
   # PTX LOAD/STORE
   (UPat((Ops.LOAD, Ops.STORE), src=(UPat(dtype=dtypes.int64),), allow_any_len=True), lambda: True),

tinygrad/uop/symbolic.py

@@ -405,8 +405,8 @@ def reduce_mul_chain(r:UOp):
   return r.replace(src=(prod(inside) if len(inside) else r.src[0].const_like(1),)+r.src[1:])*prod(outside)
 
 # this is symbolic 2.0
-REMOVE_FROM_SINK = {Ops.SINK, Ops.UNROLL, Ops.PTRCAT, Ops.CAT, Ops.NOOP}
-REMOVE_FROM_BARRIER = {Ops.VECTORIZE, Ops.SINK, Ops.CAT, Ops.PTRCAT, Ops.NOOP}
+REMOVE_FROM_SINK = {Ops.SINK, Ops.UNROLL, Ops.PTRCAT, Ops.CAT}
+REMOVE_FROM_BARRIER = {Ops.VECTORIZE, Ops.SINK, Ops.CAT, Ops.PTRCAT}
 sym = symbolic_flat+PatternMatcher([
   # LOAD/STORE -> NOOP
   (UPat.var('x').store(UPat.var('x').load(), allow_any_len=True), lambda x: None if x.dtype.addrspace != AddrSpace.REG else x.src[0].src[0]),
@@ -457,6 +457,9 @@ sym = symbolic_flat+PatternMatcher([
   (UPat().index(UPat(), UPat.const(dtypes.bool, True)).named("idx"), lambda idx: idx.replace(src=idx.src[0:2])), # remove True
   (UPat((Ops.LOAD, Ops.STORE), src=(UPat().index(UPat(), UPat.const(dtypes.bool, False)).or_casted(),), allow_any_len=True, name="x"),
     lambda x: UOp(Ops.NOOP) if x.op is Ops.STORE else x.const_like(0)), # NULL pointer store does nothing. NULL pointer load produces 0
+  # remove NOOPs from SINK
+  (UPat(Ops.SINK, name="root"),
+    lambda root: UOp(Ops.SINK, root.dtype, a, root.arg) if len(a:=tuple(x for x in root.src if x.op is not Ops.NOOP)) != len(root.src) else None),
   # remove VECTORIZE from SINK/BARRIER. TODO: SINK/BARRIER are really the same thing at GLOBAL/LOCAL levels
   (UPat(Ops.BARRIER, name="root"),
     lambda root: UOp(Ops.BARRIER, root.dtype, tuple(flatten(x.src if x.op in REMOVE_FROM_BARRIER else (x,) for x in root.src)), root.arg)

tinygrad/viz/fetch_assets.sh

@@ -10,6 +10,5 @@ fetch "dagrejs.github.io/project/dagre/latest/dagre.min.js"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/styles/default.min.css"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/highlight.min.js"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/python.min.js"
-fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/x86asm.min.js"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/cpp.min.js"
 fetch "unpkg.com/@highlightjs/[email protected]/styles/tokyo-night-dark.min.css"

tinygrad/viz/index.html

@@ -10,7 +10,6 @@
   <script src="assets/cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/highlight.min.js"></script>
   <script src="assets/cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/python.min.js"></script>
   <script src="assets/cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/cpp.min.js"></script>
-  <script src="assets/cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/x86asm.min.js"></script>
   <link rel="stylesheet" href="assets/unpkg.com/@highlightjs/[email protected]/styles/tokyo-night-dark.min.css" />
   <style>
   * {
@@ -224,14 +223,6 @@
   #device-list > div:hover {
     background-color: rgba(20, 23, 35, 0.3);
   }
-  .raw-text {
-    padding: 0 8px;
-    width: 100%;
-    height: 100%;
-  }
-  .raw-text code {
-    max-height: none !important;
-  }
   </style>
 </head>
 <body>

tinygrad/viz/js/index.js

@@ -362,7 +362,7 @@ document.getElementById("zoom-to-fit-btn").addEventListener("click", () => {
 
 // **** main VIZ interfacae
 
-function codeBlock(st, language, { loc, wrap }={}) {
+function codeBlock(st, language, { loc, wrap }) {
   const code = document.createElement("code");
   code.innerHTML = hljs.highlight(st, { language }).value;
   code.className = "hljs";
@@ -456,7 +456,7 @@ async function main() {
       for (const [j,u] of steps.entries()) {
         const inner = ul.appendChild(document.createElement("ul"));
         inner.id = `step-${i}-${j}`;
-        inner.innerText = `${u.name ?? u.loc[0].replaceAll("\\", "/").split("/").pop()+':'+u.loc[1]}`+(u.match_count ? ` - ${u.match_count}` : '');
+        inner.innerText = `${u.name ?? u.loc[0].replaceAll("\\", "/").split("/").pop()+':'+u.loc[1]} - ${u.match_count}`;
         inner.style.marginLeft = `${8*u.depth}px`;
         inner.onclick = (e) => {
           e.stopPropagation();
@@ -470,35 +470,23 @@ async function main() {
   const { currentCtx, currentStep, currentRewrite, expandSteps } = state;
   if (currentCtx == -1) return;
   const ctx = ctxs[currentCtx];
-  const step = ctx.steps[currentStep];
-  const ckey = step?.query;
+  const ckey = `ctx=${currentCtx-1}&idx=${currentStep}`;
   // close any pending event sources
   let activeSrc = null;
   for (const e of evtSources) {
-    const url = new URL(e.url);
-    if (url.pathname+url.search !== ckey) e.close();
+    if (e.url.split("?")[1] !== ckey) e.close();
     else if (e.readyState === EventSource.OPEN) activeSrc = e;
   }
   if (ctx.name === "Profiler") return renderProfiler();
   if (ckey in cache) {
     ret = cache[ckey];
   }
-  // ** Disassembly view
-  if (ckey.startsWith("/disasm")) {
-    if (!(ckey in cache)) cache[ckey] = ret = await (await fetch(ckey)).json();
-    displayGraph("profiler");
-    document.querySelector(".metadata").innerHTML = "";
-    const root = document.createElement("div");
-    root.className = "raw-text";
-    root.appendChild(codeBlock(ret.src, "x86asm"));
-    return document.querySelector(".profiler").replaceChildren(root);
-  }
-  // ** UOp view (default)
   // if we don't have a complete cache yet we start streaming rewrites in this step
+  const step = ctx.steps[currentStep];
   if (!(ckey in cache) || (cache[ckey].length !== step.match_count+1 && activeSrc == null)) {
     ret = [];
     cache[ckey] = ret;
-    const eventSource = new EventSource(ckey);
+    const eventSource = new EventSource(`/ctxs?${ckey}`);
     evtSources.push(eventSource);
     eventSource.onmessage = (e) => {
       if (e.data === "END") return eventSource.close();
@@ -517,15 +505,6 @@ async function main() {
   const metadata = document.querySelector(".metadata");
   const [code, lang] = ctx.fmt != null ? [ctx.fmt, "cpp"] : [ret[currentRewrite].uop, "python"];
   metadata.replaceChildren(codeBlock(step.code_line, "python", { loc:step.loc, wrap:true }), codeBlock(code, lang, { wrap:false }));
-  if (ctx.runtime_stats != null) {
-    const div = metadata.appendChild(document.createElement("div"));
-    div.style.maxHeight = "200px";
-    div.style.overflow = "auto";
-    for (const [i, s] of ctx.runtime_stats.entries()) {
-      const p = div.appendChild(document.createElement("p"));
-      p.innerText = `Run ${i+1} ${formatTime(s.duration)}`;
-    }
-  }
   // ** rewrite steps
   if (step.match_count >= 1) {
     const rewriteList = metadata.appendChild(document.createElement("div"));

tinygrad/viz/serve.py

@@ -1,19 +1,16 @@
 #!/usr/bin/env python3
-import multiprocessing, pickle, difflib, os, threading, json, time, sys, webbrowser, socket, argparse, socketserver, functools, codecs, io
-from contextlib import redirect_stdout
+import multiprocessing, pickle, difflib, os, threading, json, time, sys, webbrowser, socket, argparse, socketserver, functools, codecs
 from decimal import Decimal
 from http.server import BaseHTTPRequestHandler
 from urllib.parse import parse_qs, urlparse
 from typing import Any, TypedDict, Generator
 from tinygrad.helpers import colored, getenv, tqdm, unwrap, word_wrap, TRACEMETA, ProfileEvent, ProfileRangeEvent, TracingKey
 from tinygrad.uop.ops import TrackedGraphRewrite, UOp, Ops, printable, GroupOp, srender, sint
-from tinygrad.device import ProfileDeviceEvent, ProfileGraphEvent, ProfileGraphEntry, ProfilePointEvent, Device
-from tinygrad.renderer import ProgramSpec
+from tinygrad.device import ProfileDeviceEvent, ProfileGraphEvent, ProfileGraphEntry, ProfilePointEvent
 from tinygrad.dtype import dtypes
 
 uops_colors = {Ops.LOAD: "#ffc0c0", Ops.STORE: "#87CEEB", Ops.CONST: "#e0e0e0", Ops.VCONST: "#e0e0e0", Ops.REDUCE: "#FF5B5B",
                Ops.DEFINE_GLOBAL: "#ffe0b0", Ops.DEFINE_LOCAL: "#ffe0d0", Ops.DEFINE_REG: "#f0ffe0", Ops.REDUCE_AXIS: "#FF6B6B",
-               Ops.REDUCE_INTO: "#FF6B6B",
                Ops.RANGE: "#c8a0e0", Ops.ASSIGN: "#909090", Ops.BARRIER: "#ff8080", Ops.IF: "#c8b0c0", Ops.SPECIAL: "#c0c0ff",
                Ops.INDEX: "#e8ffa0", Ops.WMMA: "#efefc0", Ops.VIEW: "#C8F9D4", Ops.MULTI: "#f6ccff", Ops.KERNEL: "#3e7f55",
                **{x:"#D8F9E4" for x in GroupOp.Movement}, **{x:"#ffffc0" for x in GroupOp.ALU}, Ops.THREEFRY:"#ffff80", Ops.BUFFER_VIEW: "#E5EAFF",
@@ -28,12 +25,8 @@ ref_map:dict[Any, int] = {}
 def get_metadata(keys:list[TracingKey], contexts:list[list[TrackedGraphRewrite]]) -> list[dict]:
   ret = []
   for i,(k,v) in enumerate(zip(keys, contexts)):
-    steps = [{"name":s.name, "loc":s.loc, "depth":s.depth, "match_count":len(s.matches), "code_line":printable(s.loc),
-              "query":f"/ctxs?ctx={i}&idx={j}"} for j,s in enumerate(v)]
-    if isinstance(k.ret, ProgramSpec): steps.append({"name":"View Disassembly", "query":f"/disasm?ctx={i}"})
-    ret.append(r:={"name":k.display_name, "fmt":k.fmt, "steps":steps})
-    # use the first key to get runtime profiling data about this context
-    if getenv("PROFILE_VALUE") >= 2 and k.keys: r["runtime_stats"] = get_runtime_stats(k.keys[0])
+    steps = [{"name":s.name, "loc":s.loc, "depth":s.depth, "match_count":len(s.matches), "code_line":printable(s.loc)} for s in v]
+    ret.append({"name":k.display_name, "fmt":k.fmt, "steps":steps})
     for key in k.keys: ref_map[key] = i
   return ret
 
@@ -179,19 +172,6 @@ def get_profile(profile:list[ProfileEvent]):
   dev_layout = {k:{"timeline":timeline_layout(v), "mem":mem_layout(v)} for k,v in dev_events.items()}
   return json.dumps({"layout":dev_layout, "st":min_ts, "et":max_ts}).encode("utf-8")
 
-def get_runtime_stats(key) -> list[dict]:
-  ret:list[dict] = []
-  for e in profile:
-    if isinstance(e, ProfileRangeEvent) and e.en is not None and e.name == key:
-      ret.append({"device":e.device, "duration":float(e.en-e.st)})
-  return ret
-
-def get_disassembly(ctx:list[str]):
-  if not isinstance(prg:=contexts[0][int(ctx[0])].ret, ProgramSpec): return
-  lib = Device[prg.device].compiler.compile(prg.src)
-  with redirect_stdout(buf:=io.StringIO()): Device[prg.device].compiler.disassemble(lib)
-  return json.dumps({"src":buf.getvalue()}).encode()
-
 # ** HTTP server
 
 class Handler(BaseHTTPRequestHandler):
@@ -206,10 +186,9 @@ class Handler(BaseHTTPRequestHandler):
         if url.path.endswith(".js"): content_type = "application/javascript"
         if url.path.endswith(".css"): content_type = "text/css"
       except FileNotFoundError: status_code = 404
-    elif (query:=parse_qs(url.query)):
-      if url.path == "/disasm": ret, content_type = get_disassembly(**query), "application/json"
-      else: return self.stream_json(get_details(contexts[1][int(query["ctx"][0])][int(query["idx"][0])]))
-    elif url.path == "/ctxs": ret, content_type = json.dumps(ctxs).encode(), "application/json"
+    elif url.path == "/ctxs":
+      if "ctx" in (q:=parse_qs(url.query)): return self.stream_json(get_details(contexts[1][int(q["ctx"][0])][int(q["idx"][0])]))
+      ret, content_type = json.dumps(ctxs).encode(), "application/json"
     elif url.path == "/get_profile" and profile_ret is not None: ret, content_type = profile_ret, "application/json"
     else: status_code = 404